Synergies Between COVID-19 and Climate Change Impacts and Responses
Bibliographic record
Abstract
The COVID-19 pandemic and anthropogenic climate change are global crises. We show how strongly these crises are connected, including the underlying societal inequities and problems of poverty, substandard housing, and infrastructure including clean water supplies. The origins of all these crises are related to modern consumptive industrialisation, including burning of fossil fuels, increasing human population density, and replacement of natural with human dominated ecosystems. Because business as usual is unsustainable on all three fronts, transformative responses are needed. We review the literature on risk management interventions, implications for COVID-19, for climate change risk and for equity associated with biodiversity, water and WaSH, health systems, food systems, urbanization and governance. This paper details the considerable evidence base of observed synergies between actions to reduce pandemic and climate change risks while enhancing social justice and biodiversity conservation. It also highlights constraints imposed by governance that can impede deployment of synergistic solutions. In contrast to the response to the COVID-19 pandemic, governance systems have procrastinated on addressing climate change and biodiversity loss as these are interconnected chronic crises. It is now time to address all three to avoid a multiplication of future crises across health, food, water, nature, and climate systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".